I’m using the .apply() method in pandas. I get the same results when using axis=0 and axis=1.
When using axis=0 I’d expect a series with four elements (indexed A, B, C, D) as a result. Can anyone tell me why the axis argument doesn’t work in this case?
I’m adding a reproducible example below. NOTE: I know that this is just a simple multiplication, which could be done simply using the method .mul() or the * operator, but I’m using here to keep things simple.
from pandas import util df = util.testing.makeDataFrame() df.head()
| A | B | C | D | |
|---|---|---|---|---|
| Z82iXpl5s4 | 0.6031287648192426 | -0.8271966886193748 | 1.8008948949143067 | 2.3722926060057237 |
| MIFRqMYUeQ | 0.2607658354228718 | -0.2475715926867064 | 0.9548749309163952 | 2.682513736796777 |
| F5qTLNGXAL | -1.0716656707592347 | -0.637969249754201 | -0.6155681669722803 | 1.24399654282517 |
| 5J4v4avVEB | -0.9685885791255543 | -0.6735886163824997 | -0.8065978319692303 | 0.5227282727367413 |
| uEeRVYkeWU | -0.6661417878211171 | -0.15444679388555943 | -0.06476868167309932 | -0.8541859423835071 |
df.apply(lambda x: x * 9, axis=0).head()
| A | B | C | D | |
|---|---|---|---|---|
| Z82iXpl5s4 | 5.428158883373183 | -7.444770197574373 | 16.20805405422876 | 21.35063345405151 |
| MIFRqMYUeQ | 2.3468925188058463 | -2.2281443341803575 | 8.593874378247557 | 24.142623631170995 |
| F5qTLNGXAL | -9.644991036833112 | -5.741723247787809 | -5.540113502750523 | 11.195968885426531 |
| 5J4v4avVEB | -8.717297212129989 | -6.062297547442497 | -7.259380487723073 | 4.704554454630672 |
| uEeRVYkeWU | -5.995276090390054 | -1.3900211449700348 | -0.5829181350578939 | -7.687673481451564 |
df.apply(lambda x: x * 9, axis=1).head()
| A | B | C | D | |
|---|---|---|---|---|
| Z82iXpl5s4 | 5.428158883373183 | -7.444770197574373 | 16.20805405422876 | 21.35063345405151 |
| MIFRqMYUeQ | 2.3468925188058463 | -2.2281443341803575 | 8.593874378247557 | 24.142623631170995 |
| F5qTLNGXAL | -9.644991036833112 | -5.741723247787809 | -5.540113502750523 | 11.195968885426531 |
| 5J4v4avVEB | -8.717297212129989 | -6.062297547442497 | -7.259380487723073 | 4.704554454630672 |
| uEeRVYkeWU | -5.995276090390054 | -1.3900211449700348 | -0.5829181350578939 | -7.687673481451564 |
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Answer
Looking at the documentation for pandas.DataFrame.apply:
axis : {0 or ‘index’, 1 or ‘columns’}, default 0
Axis along which the function is applied:0 or ‘index’: apply function to each column.
1 or ‘columns’: apply function to each row.
Returns : Series or DataFrame Result of applying func along the given axis of the DataFrame.
When you are doing df.apply(lambda x: x * 9, axis=0), value in each column gets multiplied by 9, so there will be 4 series for each of the columns, and they are assigned back to the respective indices.
For more clarification, you can try something like this:
df.apply(lambda x: [(x.round(0)*9).tolist()], axis=0).T
0
A [18.0, 18.0, 0.0, -9.0, -9.0, -9.0, -0.0, -0.0...
B [-9.0, -9.0, -9.0, -9.0, -0.0, -0.0, 9.0, -18....
C [9.0, -9.0, 18.0, -0.0, 0.0, -9.0, -0.0, 0.0, ...
D [9.0, 18.0, -9.0, 9.0, -9.0, 9.0, 9.0, -9.0, 0...
Now, you’ll see those individual values.